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1.
Comput Biol Med ; 121: 103772, 2020 06.
Article in English | MEDLINE | ID: mdl-32339096

ABSTRACT

In this paper, a multiparameter cosine number transform is proposed. Such a transform is obtained using the fact that the basis vectors of the three-dimensional cosine number transform (3D-CNT) constitute a possible eigenbasis for the Laplacian of the cubical lattice graph evaluated in a finite field. The proposed transform is identified as three-dimensional steerable cosine number transform (3D-SCNT) and is defined by rotating the 3D-CNT basis vectors, using a finite field rotation operator. We introduce a 3D medical image encryption scheme based on the 3D-SCNT, which uses the rotation angles as secret parameters. By means of computer experiments, we have verified that the scheme is resistant against the main cryptographic attacks.


Subject(s)
Algorithms , Imaging, Three-Dimensional , Rotation
2.
Int J Neural Syst ; 9(3): 219-26, 1999 Jun.
Article in English | MEDLINE | ID: mdl-10560761

ABSTRACT

Vector quantization plays an important role in many signal processing problems, such as speech/speaker recognition and signal compression. This paper presents an unsupervised algorithm for vector quantizer design. Although the proposed method is inspired in Kohonen learning, it does not incorporate the classical definition of topological neighborhood as an array of nodes. Simulations are carried out to compare the performance of the proposed algorithm, named SOA (self-organizing algorithm), to that of the traditional LBG (Linde-Buzo-Gray) algorithm. The authors present an evaluation concerning the codebook design for Gauss-Markov and Gaussian sources, since the theoretic optimal performance bounds for these sources, as described by Shannon's Rate-Distortion Theory, are known. In speech and image compression, SOA codebooks lead to reconstructed (vector-quantized) signals with better quality as compared to the ones obtained by using LBG codebooks. Additionally, the influence of the initial codebook in the algorithm performance is investigated and the algorithm ability to learn representative patterns is evaluated. In a speaker identification system, it is shown that the the codebooks designed by SOA lead to higher identification rates when compared to the ones designed by LBG.


Subject(s)
Algorithms , Neural Networks, Computer , Pattern Recognition, Automated , Signal Processing, Computer-Assisted , Humans , Image Processing, Computer-Assisted , Speech
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